Balance Precision and Recall

Context

MediScan Health built a binary classifier to flag patients for follow-up diabetes screening based on routine lab results and visit history. The model is now in production across 120 clinics, but clinical leaders disagree on whether the current threshold is too conservative.

Current Performance

The Problem

The current model produces very few false alarms, but it misses a meaningful share of true diabetes-risk patients. Operations can support only a limited number of follow-up screenings each week, while clinicians argue that missed cases are more harmful than unnecessary outreach.

Task

1. Explain the difference between precision and recall using the metrics above.
2. Interpret what the current model is optimizing for and what the lower-threshold option changes.
3. Recommend when MediScan should prioritize precision versus recall.
4. Discuss the business and clinical tradeoff of changing the threshold.
5. State which configuration you would choose and why.

Constraints

• Each follow-up screening costs $45.
• Missing a true high-risk patient delays treatment and increases downstream care costs.
• Clinics can handle at most 1,200 follow-up screenings per 10,000 patients per month.
• Leadership wants a simple threshold policy that can be explained to clinicians.